Error correction decoder and memory system having the same

ABSTRACT

Provided herein may be an error correction decoder based on an iterative decoding scheme using NB-LDPC codes and a memory system having the same. The error correction decoder may include a symbol generator for assigning an initial symbol to a variable node, a reliability value manager for setting and updating reliability values of candidate symbols of the variable node in current iteration, a flipping function value calculator for calculating a flipping function value by subtracting a function value, related to the updated reliability values of remaining candidate symbols other than a target candidate symbol, from another function value, related to the updated reliability value of the target candidate symbol, in the current iteration, and a symbol corrector for changing the hard decision value to the target candidate symbol when the flipping function value is equal to or greater than a first threshold value in the current iteration.

CROSS-REFERENCE TO RELATED APPLICATION

This patent document claims priority to and benefits of the Koreanpatent application number 10-2019-0066227, filed on Jun. 4, 2019, whichis incorporated herein by reference in its entirety.

TECHNICAL FIELD

Various embodiments of the disclosed technology generally relate to anerror correction decoder using a decoding scheme, for example,non-binary low-density parity check (NB-LDPC) codes, and a memory systemhaving the error correction decoder.

BACKGROUND

A memory system may include a storage medium configured to temporarilyor permanently store data therein. During any of various operations,such as writing, reading, transmission, or processing, a data error ordata corruption may occur.

In order to ensure the reliability of data, the memory system may useerror correction techniques such as error correction encoding and errorcorrection decoding.

SUMMARY

Various embodiments of the disclosed technology relates to an errorcorrection decoder using NB-LDPC codes and a memory system having theerror correction decoder. The disclosed technology provides an improvederror correction capability when error correction decoding using NB-LDPCcodes is performed, and a memory system having the error correctiondecoder.

In one aspect, an error correction decoder is provided. The errorcorrection decoder may include a symbol generator configured to form theinitial symbol and assign the initial symbol to a variable node, areliability value manager configured to set the reliability values ofcandidate symbols corresponding to the variable node based on theinitial symbol at the time of a start of a current iteration and updatethe reliability values of the candidate symbols based oncheck-to-variable (C2V) messages received by the variable node incurrent iteration, a flipping function value calculator configured tocalculate a flipping function value by subtracting a second functionvalue from a first function value in the current iteration, the firstfunction value being related to the updated reliability value of atarget candidate symbol, and the second function value being related toone or more of updated reliability values of remaining candidate symbolsother than the target candidate symbol, and a symbol correctorconfigured to compare the flipping function value with a first thresholdvalue in the current iteration and change a hard decision value of thevariable node to the target candidate symbol upon a determination thatthe flipping function value is equal to or greater than the firstthreshold value.

In another aspect, a memory system is provided. The memory system mayinclude a memory device, and a memory controller in communication withthe memory device and including an error correction decoder configuredto perform an error correction decoding using non-binary low-densityparity check (NB-LDPC) codes based on a read vector received from thememory device. The error correction decoder may include a symbolgenerator configured to form the initial symbol by grouping read valuesincluded in the read vector and assign the initial symbol to a variablenode, a reliability value manager configured to set the reliabilityvalues of candidate symbols corresponding to the variable node based onthe initial symbol at the time of a start of a current iteration andupdate the reliability values of the candidate symbols based oncheck-to-variable (C2V) messages received by the variable node in thecurrent iteration, a flipping function value calculator configured tocalculate a flipping function value by subtracting a second functionvalue from a first function value in the current iteration, the firstfunction value being related to the updated reliability value of atarget candidate symbol, and the second function value being related toone or more of updated reliability values of remaining candidate symbolsother than the target candidate symbol from the candidate symbols, and asymbol corrector configured to compare the flipping function value witha first threshold value in the current iteration and change a harddecision value of the variable node to the target candidate symbol upona determination that the flipping function value is equal to or greaterthan the first threshold value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an error correction circuit based onsome implementations of the disclosed technology.

FIG. 2 is an example diagram illustrating a parity check matrix.

FIG. 3 is a diagram in which a parity check matrix illustrated in FIG. 2is represented as a Tanner graph.

FIG. 4 is an example diagram for explaining a syndrome vector calculatedusing a parity check matrix illustrated in FIG. 2.

FIG. 5 is an example diagram illustrating for explaining a read value.

FIG. 6 is an example diagram illustrating a symbol configuration processbased on some implementations of the disclosed technology.

FIG. 7 is an example diagram for explaining threshold values based onsome implementations of the disclosed technology.

FIG. 8 is an example diagram illustrating the process of setting areliability value based on some implementations of the disclosedtechnology.

FIGS. 9 to 15 are example diagrams illustrating the process of modifyinga hard decision value based on some implementations of the disclosedtechnology.

FIGS. 16 to 19 are flowcharts illustrating the method of operating anerror correction decoder based on some implementations of the disclosedtechnology.

FIG. 20 is a diagram illustrating a memory system based on someimplementations of the disclosed technology.

FIG. 21 is a diagram illustrating a memory device based on someimplementations of the disclosed technology.

FIG. 22 is an example diagram illustrating a memory block.

FIG. 23 and FIG. 24 are diagrams illustrating other embodiments of amemory system including a memory controller of in FIG. 20.

DETAILED DESCRIPTION

The specific structural or functional description disclosed herein ismerely illustrative for the purpose of describing embodiments. Thedisclosed technology can be implemented in various forms, and cannot beconstrued as limited to the embodiments set forth herein.

The error correction scheme using Low-density parity check (LDPC) codesare widely used for error correction in a memory system, a communicationsystem, and others because the LDPC codes can improve an errorcorrection performance without increasing the computational complexityper bit even when the length of code is increased.

There are some considerations when implementing LDPC codes with regardto the reiteration. For example, when an error occurs in LDPC codes, alarge number of iterations may be required until error correctiondecoding succeeds, which causes inefficiency and may result in adecoding failure. The disclosed technology provides techniques foraddressing the inefficiency associated with the iterations and improvingthe error correction capability.

FIG. 1 is a diagram illustrating an error correction circuit accordingto an embodiment of the disclosed technology.

Referring to FIG. 1, the error correction circuit 10 may include anerror correction encoder 100 and an error correction decoder 200.

The error correction encoder 100 receives an original message, for whicherror correction encoding is to be performed, and may perform errorcorrection encoding using the received original message and thegenerator matrix of an error correction code (ECC) or using the receivedoriginal message and the parity check matrix of the error correctioncode. The error correction encoder 100 may output a codeword, generatedas the result of performing error correction encoding, to a channel.When the error correction circuit 10 is applied in a memory system, thecodeword may be stored in a plurality of memory cells (e.g., the memorycells forming a single page) included in a memory device.

The error correction encoder 100 may be a non-binary low-density paritycheck (NB-LDPC) encoder that uses LDPC codes, particularly NB-LDPCcodes, as error correction codes, but embodiments of the presentdisclosure are not limited thereto.

The error correction decoder 200 may receive a read vector, whichcorresponds to the codeword, from the channel, and may perform errorcorrection decoding for the received read vector.

The error correction decoder 200 may perform error correction decodingusing any of various algorithms that employ an iterative decodingscheme. The error correction decoder 200 may perform error correctiondecoding using a message passing algorithm (MPA), which is referred toas a belief propagation algorithm (BPA). As the message passingalgorithm, a bit flipping algorithm, a symbol flipping algorithm, amin-sum algorithm, a sum-product algorithm, or the like may be used.Hereinafter, embodiments of the present disclosure are described on theassumption that a symbol flipping algorithm is used, but the embodimentsof the present disclosure are not limited thereto.

The error correction decoder 200 may perform error correction decodingbefore the number of iterations that are performed reaches a presetmaximum number of iterations (I). Here, the maximum number of iterations(I) may be a nature number. When a valid codeword that satisfies theconstraints of the parity check matrix of an error correction code isgenerated before the number of iterations reaches the maximum number ofiterations (I), the error correction decoder 200 may output thegenerated valid codeword as a decoded codeword. When a valid codewordthat satisfies the constraints of the parity check matrix of the errorcorrection code is not generated until the number of iterations reachesthe maximum number of iterations (I), the error correction decoder 200may output a fail signal, which indicates that error correction decodinghas failed.

The error correction decoder 200 may be an NB-LDPC decoder that usesLDPC codes, for example, NB-LDPC codes, as error correction codes, butembodiments of the disclosed technology are not limited thereto.

The error correction decoder 200 may include a symbol generator 210, anode processor 220, and a syndrome checker 230.

The symbol generator 210 may receive the read vector, which correspondsto the codeword, from the channel. The symbol generator 210 mayconfigure the initial symbols to be assigned to variable nodes bygrouping read values included in the read vector and provide the initialsymbols to the node processor 220. For example, when the read vectorincludes 14 read values, the symbol generator 210 may form seven initialsymbols, each having two read values. Each of the read values includedin the read vector may be ‘0’ or ‘1’

The node processor 220 may perform error correction decoding using amessage passing algorithm based on the initial symbols received from thesymbol generator 210. When error correction decoding is performed basedon the message passing algorithm, a result converging to the codewordmay be generated through the exchange of messages between the variablenodes and check nodes. The messages may include a variable-to-check(V2C) message, which is transmitted from a variable node to a checknode, and a check-to-variable (C2V) message, which is transmitted fromthe check node to the variable node.

The node processor 220 may perform at least one iteration before thenumber of iterations reaches the maximum number of iterations (I). Thenode processor 220 may generate a hard decision vector corresponding tothe i-th iteration, and may provide the generated hard decision vectorto the syndrome checker 230. Here, T is a natural number that is equalto or less than the maximum number of iterations (I). The hard decisionvector may include the hard decision values of the variable nodes. Atleast one of the hard decision values provided to the syndrome checker230 may be modified based on at least one of a reliability value or anunreliability value, which will be described later.

The node processor 220 may include a variable node update module 222, acheck node update module 224, and an edge gain processor 226.

Hereinafter, an example in which the node processor 220 operates basedon a flooding scheme is described, but embodiments of the disclosedtechnology are not limited thereto. For example, the node processor 220may operate based on a column-layered scheme or a row-layered scheme.

The variable node update module 222 may initialize the variable nodesusing the initial symbols received from the symbol generator 210 beforethe first iteration is performed. That is, the variable node updatemodule 222 may assign each of the initial symbols to each of thevariable nodes as the hard decision value of the variable node.

In the first iteration, the variable node update module 222 may generateV2C messages in order to transmit the hard decision values of thevariable nodes to the check nodes, and may transmit the generated V2Cmessages to the check node update module 224. The variable node updatemodule 222 may update the hard decision value of each of the variablenodes based on the C2V messages received from the check node updatemodule 224.

In each of the iterations excluding the first iteration, the variablenode update module 222 may generate V2C messages based on the C2Vmessages received from the check node update module 224 and transmit thegenerated V2C messages to the check node update module 224. Also, thevariable node update module 222 may update the hard decision values ofthe variable nodes based on the C2V messages received from the checknode update module 224.

In each iteration, the check node update module 224 may update thevalues of the check nodes based on the V2C messages received from thevariable node update module 222. Also, the check node update module 224may generate C2V messages based on the V2C messages received from thevariable node update module 222 and transmit the generated C2V messagesto the variable node update module 222.

For the messages exchanged between the variable node update module 222and the check node update module 224, edge gain processing or inverseedge gain processing may be performed. The edge gain processor 226 mayperform edge gain processing for the V2C messages generated in thevariable node update module 222 and transmit the V2C messages for whichedge gain processing is performed to the check node update module 224.The edge gain processor 226 may perform inverse edge gain processing forthe C2V messages generated in the check node update module 224 andtransmit the C2V messages for which inverse edge gain processing isperformed to the variable node update module 222. The edge gain may beacquired from the parity check matrix, and may be referred to as an edgecoefficient or an edge weight.

The variable node update module 222 may include a reliability valuemanager 222A, a flipping function value calculator 222B, a symbolcorrector 222C, and a threshold value manager 222D.

The reliability value manager 222A may manage the reliability values ofcandidate symbols corresponding to each of the variable nodes. Thereliability values of the candidate symbols may become criteria fordetermining whether to modify the hard decision value of the variablenode.

The reliability value manager 222A may set the reliability values of thecandidate symbols for each of the variable nodes when each iterationstarts. The candidate symbols are symbols that can be selected as thehard decision value of the variable node, and may include all of thesymbols included in a Galois Field GF(q).

The reliability value manager 222A may set the reliability values of thecandidate symbols in consideration of at least one of the initial symbolassigned to the variable node, the number of the iteration, or thenumber of unsatisfied check nodes (UCNs).

In an embodiment, the reliability value manager 222A may set thereliability values of the candidate symbols in consideration of thehamming distances between candidate symbols corresponding to thevariable node and the initial symbol assigned to the variable node. Thereliability value manager 222A may set the reliability values of thecandidate symbols so as to be different from each other when the hammingdistances from the initial symbol to the candidate symbols are differentfrom each other. The reliability value manger 222A may set thereliability value of the candidate symbol to a higher value as thehamming distance from the initial symbol to the candidate symbol issmaller.

For example, if a GF(4) is used, candidate symbols corresponding to avariable node may be ‘00’, ‘01’, ‘10’ and ‘11’. Here, ‘00’, ‘01’, ‘10’and ‘11’ are the binary representation of GF(4) symbols 0, 1, α and α².If the initial symbol assigned to the variable node is ‘01’, thereliability value of the candidate symbol ‘01’, which has the smallesthamming distance (0) from the initial symbol ‘01’, may be set to 3, thereliability value of each of the candidate symbols ‘00’ and ‘11’, whichhas the second smallest hamming distance (1) from the initial symbol‘01’, may be set to 1, and the reliability of the candidate symbol ‘10’,which has the largest hamming distance (2) from the initial symbol ‘01’,may be set to 0.

In an embodiment, the reliability value manager 222A may set thereliability values of the candidate symbols in consideration of thenumber of the iteration. The reliability value manager 222A may set thereliability value of at least one of the candidate symbols to a lowervalue than that in the previous iteration. In an embodiment, thereliability value manager 222A may set the reliability value of at leastone of the candidate symbols to a higher value than that in the previousiteration.

In an embodiment, the reliability value manager 222A may set thereliability values of the candidate symbols in consideration of thenumber of the iteration and the hamming distances between each of thecandidate symbols and the initial symbol. The reliability value manager222A may set the reliability value of the candidate symbol having asmaller hamming distance from the initial symbol to a lower value as thenumber of the iteration increases. In an embodiment, the reliabilityvalue manager 222A may set the reliability value of the candidate symbolhaving a smaller hamming distance from the initial symbol to a highervalue as the number of the iteration increases.

For example, assume that a GF(4) is used and that the initial symbolassigned to a variable node is ‘01’. Also, assume that, in the i-thiteration, the reliability value of the candidate symbol ‘01’ is set to3, the reliability value of each of the candidate symbols ‘00’ and ‘11’is set to 1, and the reliability value of the candidate symbol ‘10’ isset to 0. In this case, the reliability value of the candidate symbol‘01’, which has the smallest hamming distance from the initial symbol‘01’, may be set to 2 in the (i+1)-th iteration. The reliability valueof each of the remaining candidate symbols ‘00’, ‘10’ and ‘11’ may beset to the same value as that in the i-th iteration, or may be set to ahigher value than that in the i-th iteration.

In an embodiment, the reliability value manager 222A may set thereliability values of the candidate symbols in consideration of thenumber of UCNs. As the number of UCNs coupled to the variable node inthe i-th iteration is greater, the reliability value manager 222A mayset the reliability value of at least one of the candidate symbolscorresponding to the variable node to a lower value in the (i+1)-thiteration than that in the i-th iteration. In an embodiment, as thenumber of UCNs coupled to the variable node in the i-th iteration isgreater, the reliability value manager 222A may set the reliabilityvalue of at least one of the candidate symbols corresponding to thevariable node to a higher value in the (i+1)-th iteration than that inthe i-th iteration.

In an embodiment, the reliability value manager 222A may set thereliability values of the candidate symbols in consideration of thenumber of UCNs and the hamming distance. For example, as the number ofUCNs coupled to the variable node in the i-th iteration is greater, thereliability value manager 222A may set the reliability value of thecandidate symbol having a smaller hamming distance from the initialsymbol to a lower value in the (i+1)-th iteration than that in the i-thiteration. In an embodiment, as the number of UCNs coupled to thevariable node in the i-th iteration is greater, the reliability valuemanager 222A may set the reliability value of the candidate symbolhaving a smaller hamming distance from the initial symbol to a highervalue in the (i+1)-th iteration than that in the i-th iteration.

For example, assume that a GF(4) is used and that the initial symbolassigned to a variable node is ‘01’. Also, assume that, in the i-thiteration, the reliability value of the candidate symbol ‘01’ is set to3, the reliability value of each of the candidate symbols ‘00’ and ‘11’is set to 1, and the reliability value of the candidate symbol ‘10’ isset to 0. In this case, when the number of UCNs coupled to the variablenode is 20 in the i-th iteration, the reliability value of the candidatesymbol ‘01’, which has the smallest hamming distance from the initialsymbol ‘01’, may be set to 2 in the (i+1)-th iteration, and when thenumber of UCNs coupled to the variable node is 30 in the i-th iteration,the reliability value of the candidate symbol ‘01’ may be set to 1 inthe (i+1)-th iteration. The reliability value of each of the remainingcandidate symbols ‘00’, ‘10’ and ‘11’ may be set to the same value asthat in the i-th iteration, or may be set to a higher value than that inthe i-th iteration.

The reliability value manager 222A may update the reliability values ofthe candidate symbols based on the C2V messages received by the variablenode in each iteration. For example, the reliability value manager 222Amay increase the reliability value of the candidate symbol by the numberof C2V messages representing the candidate symbol. For example, thereliability value manager 222A may increase the reliability value of thecandidate symbol ‘10’ by 1 when one C2V message representing thecandidate symbol ‘10’ is received, and may increase the reliabilityvalue of the candidate symbol ‘10’ by 2 when two C2V messagesrepresenting the candidate symbol ‘10’ are received.

In each iteration, the reliability value manager 222A may provide theupdated reliability values of the candidate symbols of each of thevariable nodes to the flipping function value calculator 222B.

In each iteration, the flipping function value calculator 222B mayreceive the updated reliability values of the candidate symbols of eachof the variable nodes from the reliability value manager 222A andcalculate a flipping function value corresponding to each of thevariable nodes based on the received updated reliability values.

The flipping function value calculator 222B may calculate a firstfunction value and a second function value and calculate a flippingfunction value by subtracting the second function value from the firstfunction value. Here, the first function value may be a value related tothe updated reliability value of a target candidate symbol among thecandidate symbols, and the second function value may be a value relatedto one or more of updated reliability values of remaining candidatesymbols other than the target candidate symbol.

The flipping function value calculator 222B may select the targetcandidate symbol among the candidate symbols in order to calculate thefirst function value and the second function value. The target candidatesymbol may be selected for each of the variable nodes.

In an embodiment, the flipping function value calculator 222B may selectthe candidate symbol corresponding to the largest value, among theupdated reliability values of the candidate symbols, as the targetcandidate symbol. The flipping function value calculator 222B maycompare the updated reliability values of all of the candidate symbolswith each other in order to select the target candidate symbol.

In an embodiment, the flipping function value calculator 222B may selectthe candidate symbol corresponding to the largest value, among theupdated reliability values remaining after excluding the updatedreliability value of the candidate symbol that is the same as the harddecision value of the variable node from the updated reliability valuesof the candidate symbols, as the target candidate symbol. The flippingfunction value calculator 222B may compare the updated reliabilityvalues of the remaining candidate symbols other than the candidatesymbol that is the same as the hard decision value of the variable node,with each other in order to select the target candidate symbol. In thiscase, if the updated reliability values are compared with each other ina tournament manner, the number of comparisons may decrease by 1compared to that in the above-described embodiment.

When the target candidate symbol is selected, the flipping functionvalue calculator 222B may calculate the first function value and thesecond function value based on a preset method. The first function valueand the second function value may be calculated for each of the variablenodes.

In an embodiment, the first function value may be the updatedreliability value of the target candidate symbol.

In an embodiment, the first function value may be the updatedreliability value of the target candidate symbol to which at least oneof a scaling factor or a scaling offset is applied. The scaling factormay be a value by which the updated reliability value of the targetcandidate symbol is multiplied, and the scaling offset may be a valuethat is added to the updated reliability value of the target candidatesymbol. The scaling factor and the scaling offset may be constants orvariables. At least one of the scaling factor or the scaling offset maybe set based on at least one of the degree of the variable node or thenumber of the iteration. For example, at least one of the scaling factoror the scaling offset may be set larger as the degree of the variablenode is larger or as the number of the iteration increases.

In an embodiment, the second function value may be the second largestvalue among the updated reliability values of the candidate symbols.

In an embodiment, the second function value may be a value calculated byadding at least two of the updated reliability values remaining afterexcluding the largest value from the updated reliability values of thecandidate symbols. For example, the second function value may be a valuecalculated by adding the second largest value and the third largestvalue, among the updated reliability values of the candidate symbols.For example, the second function value may be a value calculated byadding all of the values remaining after excluding the largest valuefrom the updated reliability values of the candidate symbols.

In an embodiment, the second function value may be the updatedreliability value of the candidate symbol that is the same as the harddecision value of the variable node.

When the first function value and the second function value arecalculated, the flipping function value calculator 222B may calculate aflipping function value by subtracting the second function value fromthe first function value. The flipping function value calculator 222Bmay provide the calculated flipping function value to the symbolcorrector 222C. The flipping function value may be calculated andprovided for each of the variable nodes. Here, the flipping functionvalue calculator 222B may further provide information about the targetcandidate symbol to the symbol corrector 222C.

The symbol corrector 222C may modify or maintain the hard decisionvalues of the variable nodes based on the flipping function valuesreceived from the flipping function value calculator 222B.

The symbol corrector 222C may determine whether the flipping functionvalue is equal to or greater than a first threshold value by comparingthe flipping function value with the first threshold value for each ofthe variable nodes. The first threshold value may be a positive number.When the flipping function value is equal to or greater than the firstthreshold value, this may indicate that the target candidate symbol is amore accurate estimate for the variable node. Therefore, the symbolcorrector 222C may change the hard decision value of the variable nodeto the target candidate symbol when the flipping function value is equalto or greater than the first threshold value. In some implementations,the symbol corrector 222C may set the unreliability value of thevariable node to an initial value, which will be described later. Thesymbol corrector 222C may not change the hard decision value of thevariable node when the flipping function value is less than the firstthreshold value.

In an embodiment, the symbol corrector 222C may skip the comparison withthe first threshold value when the flipping function value correspondingto the variable node is not a positive number.

In an embodiment that the flipping function value corresponding to thevariable node is not a positive number, the symbol corrector 222C maydetermine whether the absolute value of the flipping function value isequal to or greater than the first threshold value by comparing theabsolute value of the flipping function value with the first thresholdvalue. For example, when the first function value is related to thelargest value among the remaining updated reliability values other thanthe updated reliability value of the candidate symbol that is the sameas the hard decision value of the variable node, and when the secondfunction value is related to the updated reliability value of thecandidate symbol that is the same as the hard decision value of thevariable node, if the flipping function value is not a positive numberand the absolute number thereof is equal to or greater than the firstthreshold value, this may indicate that the hard decision value of thevariable node is a more accurate estimate. Therefore, the symbolcorrector 222C does not change the hard decision value of the variablenode, and may set the unreliability value of the variable node to aninitial value, which will be described later.

Meanwhile, in order to ensure the reliability of the variable nodes, forwhich symbol correction is not performed because the flipping functionvalue is less than the first threshold value, additional information maybe used. This information is referred to as the unreliability value ofthe variable node in embodiments of the disclosed technology.

The symbol corrector 222C may manage the unreliability values of therespective variable nodes. The symbol corrector 222C may set theunreliability values of the respective variable nodes to initial valueswhen the first iteration starts. For example, the symbol corrector 222Cmay set the unreliability values of the respective variable nodes to thesame initial value, for example, 0, when the first iteration starts. Thesymbol corrector 222C may update the unreliability value of a variablenode depending on whether the flipping function value calculated for thevariable node in an iteration is equal to or greater than apredetermined threshold value.

In order to represent the unreliability value, one bit or two or morebits may be allocated to each of the variable nodes, and the tworespective cases are described hereinafter.

First, the case in which one bit is allocated in order to represent anunreliability value is described.

The symbol corrector 222C may determine whether the flipping functionvalue is equal to or greater than a second threshold value, which isless than the first threshold value, for each of the variable nodes inwhich the flipping function value is less than the first thresholdvalue. The second threshold value may be a positive number. When theflipping function value corresponding to the variable node is equal toor greater than the second threshold value and less than the firstthreshold value, the symbol corrector 222C may check whether theunreliability value of the corresponding variable node is changed froman initial value. For example, the symbol corrector 222C may checkwhether the bit allocated for representing the unreliability value ischanged to 1 from the initial value 0.

When the unreliability value is not changed in the variable node, ofwhich the flipping function value is equal to or greater than the secondthreshold value and less than the first threshold value, the symbolcorrector 222C may change the unreliability value of the correspondingvariable node. For example, the symbol corrector 222C may change theunreliability value of the corresponding variable node to 1 from theinitial value 0. The unreliability value may be regarded as representingthe possibility that the current hard decision value of the variablenode is an error.

When the unreliability value is changed from the initial value in thevariable node, of which the flipping function value is equal to orgreater than the second threshold value and less than the firstthreshold value, the symbol corrector 222C may change the hard decisionvalue of the corresponding variable node. For example, the symbolcorrector 222C may change the hard decision value of the variable node,in which the unreliability value is changed from the initial value, tothe target candidate symbol corresponding to the current iteration. Inan embodiment, when it changes the hard decision value of the variablenode based on the unreliability value, the symbol corrector 222C may setthe unreliability value of the corresponding variable node to theinitial value. In an embodiment, even though it changes the harddecision value of the variable node based on the unreliability value,the symbol corrector 222C may not change the unreliability value of thecorresponding variable node. This is because, when the flipping functionvalue is equal to or greater than the second threshold value and lessthan the first threshold value, the target candidate symbol may be aless accurate estimate for the variable node than when the flippingfunction value is equal to or greater than the first threshold value.Therefore, in this case, the unreliability value of the variable nodemay not be changed in order to easily change the hard decision value ofthe variable node later.

Next, the case in which two or more bits are allocated in order torepresent an unreliability value is described.

In an embodiment, the symbol corrector 222C may determine whether theflipping function value is equal to or greater than the second thresholdvalue, which is less than the first threshold value, for each of thevariable nodes, of which the flipping function value is less than thefirst threshold value. When the flipping function value corresponding tothe variable node is equal to or greater than the second threshold valueand less than the first threshold value, the symbol corrector 222C mayupdate the unreliability value of the corresponding variable node. Forexample, the symbol corrector 222C may increase the unreliability valueof the variable node by a preset value. For example, the symbolcorrector 222C may increase the unreliability value of the variable nodeby 1 or by the flipping function value.

In an embodiment, the symbol corrector 222C may determine whether theflipping function value is equal to or greater than the fourth to n-ththreshold values, which are less than the first threshold value, foreach of the variable nodes, of which the flipping function value is lessthan the first threshold value (where n is a natural number that isequal to or greater than 5). For example, when the fourth thresholdvalue and the fifth threshold value, which is less than the fourththreshold value, are used, the symbol corrector 222C may increase theunreliability value of the variable node by a first preset value whenthe flipping function value is equal to or greater than the fourththreshold value, and may increase the unreliability value of thevariable node by a second preset value when the flipping function valueis equal to or greater than the fifth threshold value and less than thefourth threshold value. Here, the first preset value may be greater thanthe second preset value.

When the unreliability value of the variable node is updated, the symbolcorrector 222C may determine whether the updated unreliability value isequal to or greater than the third threshold value. The third thresholdvalue may be a positive number. When the updated unreliability value isequal to or greater than the third threshold value, the symbol corrector222C may change the hard decision value of the variable node to thetarget candidate symbol corresponding to the current iteration. Based onthe same principle as in the above-described example, the symbolcorrector 222C may or may not set the unreliability value of thevariable node to an initial value when it changes the hard decisionvalue of the variable node based on the unreliability value.

The symbol corrector 222C may provide the hard decision values of thevariable nodes to the syndrome checker 230 in each iteration. At leastone of the hard decision values of the variable nodes provided to thesyndrome checker 230 may be the value modified based on the reliabilityvalue or the unreliability value.

The threshold value manager 222D may set threshold values (e.g., firstto n-th threshold values), which are criteria for determining whether tomodify the variable node value. The threshold values may be set only inthe first iteration, or may be additionally set in at least one of thesubsequent iterations. The threshold value manager 222D may set thethreshold values in consideration of at least one of the degree of thevariable node, the number of the iteration, or the number of UCNscorresponding to the previous iteration.

For example, the threshold value manager 222D may set at least one ofthe first to n-th threshold values higher as the degree of the variablenode is higher, and may set at least one of the first to n-th thresholdvalues lower as the degree of the variable node is lower.

For example, the threshold value manager 222D may set at least one ofthe first to n-th threshold values higher as the number of the iterationincreases. In an embodiment, the threshold value manager 222D may set atleast one of the first to n-th threshold values lower as the number ofthe iteration increases.

For example, the threshold value manager 222D may set at least one ofthe first to n-th threshold values higher as the number of UCNscorresponding to the previous iteration is greater. In an embodiment,the threshold value manager 222D may set at least one of the first ton-th threshold values lower as the number of UCNs corresponding to theprevious iteration is greater.

The syndrome checker 230 may perform a syndrome check for the harddecision values (hard decision vector) received from the node processor220 in response to the i-th iteration. For example, the syndrome checkmay be performed by checking whether all of the entries of a syndromevector S_(i) calculated through Equation (1) are 0.

S _(i) =H·C _(i) ^(T)  (1)

Here, S_(i) denotes the syndrome vector corresponding to the i-thiteration, H denotes the parity check matrix of an error correctioncode, and C_(i) ^(T) denotes the transpose of the hard decision vectorC_(i) corresponding to the i-th iteration. Here, the hard decisionvector C_(i) is assumed to be a row vector.

When all of the entries of the syndrome vector S_(i) are 0, thisindicates that the syndrome check has passed, and when there is an entrythat is not 0, among all of the entries of the syndrome vector S_(i),this indicates that the syndrome check has failed.

When the syndrome check passes, the syndrome checker 230 may output thehard decision vector received from the node processor 220 as the decodedcodeword.

When the syndrome check fails, the syndrome checker 230 may provideinformation about the number of unsatisfied check nodes (UCNs)corresponding to the syndrome vector S_(i) to the node processor 220.Here, the UCNs may correspond to the entries that are not 0, among theentries of the syndrome vector S_(i).

When the syndrome check does not pass until the number of iterationsreaches the maximum number of iterations (I), the syndrome checker 230may output a fail signal, which indicates that error correction decodinghas failed.

FIG. 2 is an example diagram illustrating a parity check matrix.

FIG. 2 illustrates an example of a parity check matrix H that defines an(n, k) code. The (n, k) code may be defined by a parity check matrixhaving a size of (n−k)×n. Each of the entries of the parity check matrixmay be represented as an element included in a Galois field.

The Galois field GF(q) is a finite field having q elements, and theelements of the Galois field GF(q) may be represented as {0, α⁰, α¹, . .. , α^(q-2)} When the number of nonzero entries α⁰, α¹, . . . , α^(q-2)included in the parity check matrix is much smaller than the number ofzeros, the (n, k) code may be referred to as an (n, k) LDPC code. Here,n and k may be natural numbers.

A binary LDPC code may have the elements of a GF(2) as the entriesthereof, and a non-binary (NB) LDPC code may have the elements of aGF(q) as the entries thereof, (where q>2). FIG. 2 illustrates an exampleof the parity check matrix of an NB-LDPC code, which has the elements ofa GF(4) as the entries thereof.

FIG. 3 is a diagram in which the parity check matrix illustrated in FIG.2 is represented as a Tanner graph.

An (n, k) code may be represented as a Tanner graph, which is expressedas a bipartite graph. The Tanner graph may be represented using checknodes, variable nodes, and edges. The check nodes correspond to the rowsof the parity check matrix, and the variable nodes correspond to thecolumns thereof. Each of the edges couples one check node to onevariable node, and corresponds to a nonzero entry of the parity checkmatrix.

As illustrated in FIG. 3, the parity check matrix of the (n, k) codeillustrated in FIG. 2 may be represented as a Tanner graph including(n−k) check nodes CN₁ to CN_(n-k) and n variable nodes VN₁ to VN_(n).The solid lines and the dotted lines coupling the check nodes CN₁ toCN_(n-k) to the variable nodes VN₁ to VN_(n) represent edges.

Iterative decoding may be performed through the exchange of messagesbetween the check nodes CN₁ to CN_(n-k) and the variable nodes VN₁ toVN_(n) on the Tanner graph illustrated in FIG. 3 according to a messagepassing algorithm. The variable nodes may perform error correction usingC2V messages received from the check nodes coupled thereto, and thecheck nodes may perform a parity check using V2C messages received fromthe variable nodes coupled thereto. If the result of an exclusive OR(XOR) operation performed on the hard decision values of all of thevariable nodes coupled to any one check node includes only Os, the checknode may be regarded as being satisfied. Conversely, if the result of anXOR operation performed on the hard decision values of all of thevariable nodes coupled to any one check node includes an element that isnot 0, the check node may be regarded as being unsatisfied, and may bereferred to as an UCN. Here, the hard decision values of the variablenodes on which an XOR operation is performed may be the values on whichedge gain processing is performed.

FIG. 4 is an example diagram for explaining a syndrome vector that iscalculated using the parity check matrix illustrated in FIG. 2.

As described above, a syndrome vector S_(i) may be generated based onthe parity check matrix H and the transpose C_(i) ^(T) of the harddecision vector C_(i) corresponding to the i-th iteration. The entriesS_(i1), S_(i2), . . . , S_(in-k) of the syndrome vector S_(i) correspondto the check nodes CN₁ to CN_(n-k) on the Tanner graph illustrated inFIG. 3.

When all of the entries S_(i1), S_(i2), . . . , S_(in-k) of the syndromevector S_(i) are 0, this indicates that the syndrome check has passed inthe i-th iteration. This means that error correction decoding issuccessfully performed in the i-th iteration. Accordingly, iterativedecoding for the current read vector is terminated, and the harddecision vector C_(i) corresponding to the i-th iteration may be outputas a decoded codeword.

If at least one of the entries S_(i1), S_(i2), . . . , S_(in-k) of thesyndrome vector S_(i) is not 0, this indicates that the syndrome checkhas failed in the i-th iteration. This means that error correctiondecoding has failed in the i-th iteration, and the next iteration may beperformed if the number of the iteration does not reach the maximumnumber of iterations (I). If the number of the iteration reaches themaximum number of iterations (I), iterative decoding for the currentread vector may be terminated.

FIG. 5 is an example diagram for explaining a read value.

FIG. 5 illustrates the distribution of the threshold voltage Vth ofmemory cells, each of which has any one state among a first state S1 anda second state S2.

In order to acquire a single read vector corresponding to a singlecodeword, one read voltage Vr1 may be applied to a plurality of memorycells, which store one codeword (e.g., the memory cells of a singlepage). Accordingly, one read value may be acquired for each memory cell.A single read vector may include the read values corresponding to themultiple memory cells.

For example, when the first read voltage Vr1 is applied to a pluralityof memory cells, the read value for the memory cell having a thresholdvoltage that is lower than the first read voltage Vr1 may be representedas ‘1’, and the read value for the memory cell having a thresholdvoltage that is higher than the first read voltage Vr1 may berepresented as ‘0’.

FIG. 6 is an example diagram illustrating the process of configuring asymbol according to an embodiment of the disclosed technology.

In the embodiment described with reference to FIG. 6, it is assumed thatthe read vector received from the channel includes 14 read values.

The error correction decoder may form a plurality of symbols by groupingthe read values included in the read vector into the symbols having apreset number of read values. For example, when a GF(4) is used, theerror correction decoder may form a single symbol by grouping two readvalues. Because the read vector includes 14 read values, the errorcorrection decoder may form a total of seven symbols. The errorcorrection decoder may assign the corresponding symbols to the variablenodes VN₁ to VN₇, respectively.

Hereinafter, it is assumed that the binary representation ‘00’corresponds to the GF(4) representation ‘0’, the binary representation‘01’ corresponds to the GF(4) representation ‘1’, the binaryrepresentation ‘10’ corresponds to the GF(4) representation ‘α’, and thebinary representation ‘11’ corresponds to the GF(4) representation ‘α²’.

FIG. 7 is an example diagram for explaining threshold values accordingto an embodiment of the disclosed technology.

As described above, for each of the variable nodes, one or morethreshold values can be set based on whether an unreliability value isused and how many bits are allocated to an unreliability value, if any.

In case that a reliability value is used only without an unreliabilityvalue, a first threshold value is set for each of the variable nodes.

In case that a reliability value and an unreliability value are usedtogether and one bit is allocated to each of the variable nodes in orderto represent the unreliability value, a first threshold value and asecond threshold value are set for each of the variable nodes.

In case that a reliability value and an unreliability value are usedtogether and two or more bits are allocated to each of the variablenodes in order to represent the unreliability value, first to n-ththreshold values are set for each of the variable nodes.

Meanwhile, as described above, at least one of the threshold values maybe set based on at least one of the degree of the variable node, thenumber of the iteration, or the number of UCNs corresponding to theprevious iteration.

FIG. 7 illustrates an example in which the first to third thresholdvalues are set depending on the degree of the variable node. Referringto FIG. 7, it may be confirmed that higher threshold values are set forthe variable nodes VN₁ and VN₃ having a higher degree (32), and lowerthreshold values are set for the variable nodes VN₂ and VN_(n) having alower degree (26).

FIG. 8 is an example diagram illustrating a process of setting areliability value according to an embodiment of the disclosedtechnology.

For the convenience of description, FIG. 8 illustrates only somevariable nodes and some check nodes on the Tanner graph. For theconvenience of description, only two variable nodes VN₁ and VN₂ aredescribed in embodiments with reference to FIG. 8, but the samedescription can be also applied to the remaining variable nodes VN₃ toVN_(n).

In the embodiment described with reference to FIG. 8, it is assumed thata GF(4) is used, the initial symbol ‘1’ is assigned to the variable nodeVN₁, the initial symbol ‘0’ is assigned to the variable node VN₂, theinitial symbol ‘α’ is assigned to the variable node VN₃, and the initialsymbol ‘α²’ is assigned to the variable node VN_(n).

As described above, the reliability values of the candidate symbols maybe set based on at least one of the hamming distances between each ofthe candidate symbols and the initial symbol, the number of theiteration, or the number of UCNs corresponding to the previousiteration.

FIG. 8 illustrates an example in which the reliability values of thecandidate symbols are set based on the hamming distances between each ofthe candidate symbols and the initial symbol.

In the case of the variable node VN₁, the reliability value of thecandidate symbol ‘1’ is set to the highest value, 3, because the hammingdistance from the initial symbol ‘1’ is 0, which is the smallest hammingdistance, the reliability value of each of the candidate symbols ‘0’ and‘α²’ is set to the second highest value, 1, because the hamming distancefrom the initial symbol ‘1’ is 1, which is the second smallest hammingdistance, and the reliability value of the candidate symbol ‘α’ is setto the lowest value, 0, because the hamming distance from the initialsymbol ‘1’ is 2, which is the largest hamming distance.

In the case of the variable node VN₂, the reliability value of thecandidate symbol ‘0’ is set to the highest value, 3, because the hammingdistance from the initial symbol ‘0’ is 0, which is the smallest hammingdistance, the reliability value of each of the candidate symbols ‘1’ and‘α’ is set to the second highest value, 1, because the hamming distancefrom the initial symbol ‘0’ is 1, which is the second smallest hammingdistance, and the reliability value of the candidate symbol ‘α²’ is setto the lowest value, 0, because the hamming distance from the initialsymbol ‘0’ is 2, which is the largest hamming distance.

FIG. 9 is an example diagram illustrating the process of modifying ahard decision value according to an embodiment of the disclosedtechnology.

In each iteration, the reliability values of the candidate symbolscorresponding to each of the variable nodes VN₁ to VN_(n) may be updateddepending on the C2V messages input for each of the variable nodes VN₁to VN_(n). For example, the reliability value of a predeterminedcandidate symbol may increase based on the number of received C2Vmessages representing the predetermined candidate symbol. For example,when one C2V message representing the candidate symbol ‘α’ is received,the reliability value of the candidate symbol ‘α’ may increase by 1, andwhen two C2V messages representing the candidate symbol ‘α’ arereceived, the reliability value of the candidate symbol ‘α’ may increaseby 2.

In the embodiment described with reference to FIG. 9, it is assumedthat, after the setting process described with reference to FIG. 8, thevariable node VN₁ receives six C2V messages representing the candidatesymbol ‘0’, two C2V messages representing the candidate symbol ‘1’, 22C2V messages representing the candidate symbol ‘α’, and two C2V messagesrepresenting the candidate symbol ‘α²’. Also, it is assumed that thevariable node VN₂ receives nine C2V messages representing the candidatesymbol ‘0’, seven C2V messages representing the candidate symbol ‘1’,five C2V messages representing the candidate symbol ‘α’, and five C2Vmessages representing the candidate symbol ‘α²’.

Accordingly, the reliability value for the candidate symbol ‘0’ of thevariable node VN₁ may be updated from 1 to 7, the reliability value forthe candidate symbol ‘1’ of the variable node VN₁ may be updated from 3to 5, the reliability value for the candidate symbol ‘α’ of the variablenode VN₁ may be updated from 0 to 22, and the reliability value for thecandidate symbol ‘α²’ of the variable node VN₁ may be updated from 1 to3.

Also, the reliability value for the candidate symbol ‘0’ of the variablenode VN₂ may be updated from 3 to 12, the reliability value for thecandidate symbol ‘1’ of the variable node VN₂ may be updated from 1 to8, the reliability value for the candidate symbol ‘α’ of the variablenode VN₂ may be updated from 1 to 6, and the reliability value for thecandidate symbol ‘α²’ of the variable node VN₂ may be updated from 0 to5.

After the reliability values are updated, a flipping function value maybe calculated for each of the variable nodes. As described above, theflipping function value may be calculated by subtracting a secondfunction value from a first function value, the first function valuebeing related to the updated reliability value of a target candidatesymbol and the second function value being related to one or more ofupdated reliability values of the remaining candidate symbols other thanthe target candidate symbol.

In the embodiment described with reference to FIG. 9, the targetcandidate symbol is assumed to be the candidate symbol corresponding tothe largest value, among the updated reliability values. Also, the firstfunction value is assumed to be the updated reliability value of thetarget candidate symbol which is the largest value among the updatedreliability values of the candidate symbols. Also, the second functionvalue is assumed to be the second largest value among the updatedreliability values.

In the example of FIG. 9, for the variable node VN₁, the largest valueamong the updated reliability values of the candidate symbols is 22 andthe second largest value is 7. Thus, the first function value becomes22, and the second function value becomes 7. Accordingly, the flippingfunction value becomes 15, which is calculated by subtracting the secondfunction value (7) from the first function value (22). When the flippingfunction value is equal to or greater than the first threshold value,the hard decision value of the variable node VN₁ may be changed to thetarget candidate symbol. Because the first threshold value correspondingto the variable node VN₁ is 7 and the flipping function value is 15, thehard decision value of the variable node VN₁ may be changed to thetarget candidate symbol, that is, ‘α’.

In the example of FIG. 9, for the variable node VN₂, the largest valueamong the updated reliability values of the candidate symbols is 12 andthe second largest value is 8. That is, the first function value becomes12, and the second function value becomes 8. Accordingly, the flippingfunction value becomes 4, which is calculated by subtracting the secondfunction value (8) from the first function value (12). When the flippingfunction value is less than the first threshold value, the hard decisionvalue of the variable node VN₂ may be maintained. Because the firstthreshold value corresponding to the variable node VN₂ is 6 and theflipping function value is 4, the hard decision value of the variablenode VN₂ may be maintained without any changes.

FIG. 10 is an example diagram illustrating a process of modifying a harddecision value according to an embodiment of the disclosed technology.

In explaining the embodiment described with reference to FIG. 10, adescription which has been explained with reference to FIG. 9 will beomitted.

In the embodiment described with reference to FIG. 10, it is assumedthat, after the setting process described with reference to FIG. 8, thereliability values of the candidate symbols of the variable nodes VN₁and VN₂ are updated depending on the C2V messages received by thevariable nodes VN₁ and VN₂. The reliability values of the candidatesymbols of the variable nodes VN₁ and VN₂ are assumed to be updated inthe same manner as described with reference to FIG. 9.

In the embodiment described with reference to FIG. 10, the targetcandidate symbol is the candidate symbol whose updated reliability valueis largest among the updated reliability values of candidate symbols,and the first function value is assumed to be the updated reliabilityvalue of the target candidate symbol.

In the embodiment described with reference to FIG. 10, the secondfunction value is assumed to be the updated reliability value of thecandidate symbol that is the same as the current hard decision value.

In the example of FIG. 10, for the variable node VN₁, the largest valueamong the updated reliability values of the candidate symbols is 22, andthe updated reliability value of the candidate symbol ‘1’ that is thesame as the hard decision value of the variable node VN₁ is 5. Thus, thefirst function value becomes 22, and the second function value becomes5. Accordingly, the flipping function value becomes 17, which iscalculated by subtracting the second function value (5) from the firstfunction value (22). When the flipping function value is equal to orgreater than the first threshold value, the hard decision value of thevariable node VN₁ may be changed to the target candidate symbol. Becausethe first threshold value corresponding to the variable node VN₁ is 7and the flipping function value is 17, the hard decision value of thevariable node VN₁ may be changed to the target candidate symbol, thatis, ‘α’.

In the example of FIG. 10, for the variable node VN₂, the largest valueamong the updated reliability values of the candidate symbols is 12corresponding to the candidate symbol ‘0.’ Thus, the candidate symbolhaving the largest updated reliability value is the same as the currenthard decision value. In this case, the flipping function value is notcalculated, and the hard decision value of the variable node VN₂ may bemaintained without any changes.

FIG. 11 is an example diagram illustrating a process of modifying a harddecision value according to an embodiment of the disclosed technology.

In explaining the embodiment described with reference to FIG. 11, αdescription which has been explained with reference to FIG. 9 will beomitted.

In the embodiment described with reference to FIG. 11, it is assumedthat, after the setting process described with reference to FIG. 8, thereliability values of the candidate symbols of the variable nodes VN₁and VN₂ are updated depending on the C2V messages received by thevariable nodes VN₁ and VN₂. The reliability values of the candidatesymbols of the variable nodes VN₁ and VN₂ are assumed to be updated inthe same manner as is described with reference to FIG. 9.

In the embodiment described with reference to FIG. 11, the targetcandidate symbol is the candidate symbol which is not same as thecurrent hard decision value and whose updated reliability value islargest among the remaining updated reliability values. Thus, the targetcandidate symbol has the largest updated reliability value among theupdated reliability values of the candidate symbols other than thecandidate symbol that is the same as the current hard decision value.The first function value is assumed to be the updated reliability valueof the target candidate symbol.

In the embodiment described with reference to FIG. 11, the secondfunction value is assumed to be the updated reliability value of thecandidate symbol that is the same as the current hard decision value.

In the example of FIG. 11, for the variable node VN₁, since the currenthard decision value is 1, the candidate symbol ‘1,’ which is same as thecurrent hard decision value, is excluded from the target candidatesymbol. The largest updated reliability value is 22 and thecorresponding candidate symbol is ‘α’ (which is not same as the currenthard decision value of 1). Thus, ‘α’ become the target candidate symboland the first function value becomes 22. The updated reliability valueof the candidate symbol ‘1’ that is the same as the current harddecision value is 5. Thus, the second function value becomes 5.Accordingly, the flipping function value becomes 17, which is calculatedby subtracting the second function value (5) from the first functionvalue (22). When the flipping function value is equal to or greater thanthe first threshold value, the hard decision value of the variable nodeVN₁ may be changed to the target candidate symbol. Because the firstthreshold value corresponding to the variable node VN₁ is 7 and theflipping function value is 17, the hard decision value of the variablenode VN₁ may be changed to the target candidate symbol, that is, ‘α’.

In the example of FIG. 11, for the variable node VN₂, since the currenthard decision value is ‘0,’ the candidate symbol ‘0,’ which is same asthe current hard decision value, is excluded from the target candidatesymbol. The largest updated reliability value is 12 but thecorresponding candidate symbol is ‘0’ which is same as the current harddecision value of 0. Among the remaining updated reliability valuesother than the updated reliability value of the candidate symbol ‘0’that is the same as the current hard decision value, the largest valueis 8. The updated reliability value of the candidate symbol ‘0’ that isthe same as the current hard decision value is 12. Accordingly, theflipping function value becomes −4, which is calculated by subtractingthe second function value (12) from the first function value (8). In theembodiment described with reference to FIG. 11, when the flippingfunction value is not a positive number, this may mean that the currenthard decision value is a reliable estimate. Therefore, in this case, thecomparison of the flipping function value with the first threshold valueis skipped, and the hard decision value of the variable node VN₂ may bemaintained.

FIG. 12 is an example diagram illustrating the process of modifying ahard decision value according to an embodiment of the disclosedtechnology.

In the embodiment described with reference to FIG. 12, the unreliabilityvalues of the variable nodes are additionally used, and it is assumedthat one bit is allocated to each of the variable nodes in order torepresent the unreliability value. Also, it is assumed that theunreliability values are initially set to an initial value 0.

In explaining the embodiment described with reference to FIG. 12, adescription which has been explained with reference to FIG. 9 will beomitted.

In the embodiment described with reference to FIG. 12, it is assumedthat, after the setting process described with reference to FIG. 8, thereliability values of the candidate symbols of the variable nodes VN₁and VN₂ are updated based on the C2V messages received by the variablenodes VN₁ and VN₂. The reliability values of the candidate symbols ofthe variable nodes VN₁ and VN₂ are assumed to be updated in the samemanner as is described with reference to FIG. 9.

In the embodiment described with reference to FIG. 12, the targetcandidate symbol is the candidate symbol whose updated reliability valueis largest among the updated reliability values of candidate symbols,and the first function value is assumed to be the updated reliabilityvalue of the target candidate symbol.

In the embodiment described with reference to FIG. 12, the secondfunction value is assumed to be the second largest value, among theupdated reliability values.

For the variable node VN₁, the symbol correction may be performed basedon the same principle as that described with reference to FIG. 9.

In the example of FIG. 12, for the variable node VN₂, the largest valueamong the updated reliability values of the candidate symbols is 12 andthe second largest value is 8. Thus, the first function value becomes12, and the second function value becomes 8. Accordingly, the flippingfunction value becomes 4, which is calculated by subtracting the secondfunction value (8) from the first function value (12). When the flippingfunction value is equal to or greater than the second threshold valueand less than the first threshold value and when the unreliability valueof the variable node VN₂ is not changed from the initial value, theunreliability value of the variable node VN₂ may be changed. For thevariable node VN₂, the first threshold value is 6, the second thresholdvalue is 4, the flipping function value is 4, and the unreliabilityvalue of the variable node VN₂ is not changed from the initial value 0.Accordingly, the unreliability value of the variable node VN₂ may bechanged to 1. Also, the hard decision value of the variable node VN₂ maybe maintained.

FIG. 13 is an example diagram illustrating the process of modifying ahard decision value according to an embodiment of the disclosedtechnology.

FIG. 13 illustrates an example in which, after symbol correction isperformed and the unreliability value is set as described with referenceto FIG. 12, C2V messages are received in the subsequent iteration inwhich the reliability value is set and the unreliability value ismaintained.

In the embodiment described with reference to FIG. 13, the reliabilityvalues of the candidate symbols may be updated based on a similar orsame principle as in the above-described embodiments, and a detaileddescription thereof will be omitted.

In the embodiment described with reference to FIG. 13, the targetcandidate symbol is the candidate symbol whose updated reliability valueis the largest value among the updated reliability values, and the firstfunction value is assumed to be the updated reliability value of thetarget candidate symbol.

In the embodiment described with reference to FIG. 13, the secondfunction value is assumed to be the second largest value among theupdated reliability values.

For the variable node VN₁, the hard decision value may be modified basedon a similar or same principle as that described with reference to FIG.9.

In the example of FIG. 13, for the variable node VN₂, the largest valueamong the updated reliability values of the candidate symbols is 13 andthe second largest value is 8. Thus, the first function value becomes13, and the second function value becomes 8. Accordingly, the flippingfunction value becomes 5, which is calculated by subtracting the secondfunction value (8) from the first function value (13). When the flippingfunction value is equal to or greater than the second threshold valueand less than the first threshold value and when the unreliability valueof the variable node VN₂ is changed from an initial value, the harddecision value of the variable node VN₂ may be changed to the targetcandidate symbol. For the variable node VN₂, the first threshold valueis 6, the second threshold value is 4, the flipping function value is 4,and the unreliability value of the variable node VN₂ is changed from theinitial value. Accordingly, the hard decision value of the variable nodeVN₂ may be changed to the target candidate symbol ‘α’. FIG. 13illustrates an example in which, after the hard decision value of thevariable node VN₂ is modified based on the unreliability value, theunreliability value of the variable node VN₂ is changed to 0 from 1. Insome embodiment, even though the hard decision value of the variablenode VN₂ is modified based on the unreliability value, the unreliabilityvalue of the variable node VN₂ may not be changed.

FIG. 14 is an example diagram illustrating a process of modifying a harddecision value according to an embodiment of the disclosed technology.

In explaining the embodiment described with reference to FIG. 14, it isassumed that two or more bits are allocated to each of the variablenodes in order to represent an unreliability value. Also, it is assumedthat the unreliability values are initially set to an initial value 0.

In the embodiment described with reference to FIG. 14, it is assumedthat, after the setting process described with reference to FIG. 8, thereliability values of the candidate symbols of the variable nodes VN₁and VN₂ are updated based on the C2V messages received by the variablenodes VN₁ and VN₂. In the embodiment described with reference to FIG.14, the reliability values of the candidate symbols may be updated basedon a similar or same principle as in the above-described embodiments,and a detailed description thereof will be omitted.

In the embodiment described with reference to FIG. 14, the targetcandidate symbol is the candidate symbol whose updated reliability valueis the largest among the updated reliability values, and the firstfunction value is assumed to be the updated reliability value of thetarget candidate symbol. Also, the second function value is assumed tobe the second largest value among the updated reliability values.

In the example of FIG. 14, for the variable node VN₁, the flippingfunction value becomes 5, which is calculated by subtracting the secondfunction value (12) from the first function value (17), based on thesame principle as in the above-described embodiments. When the flippingfunction value is equal to or greater than the second threshold valueand less than the first threshold value, the unreliability value of thevariable node VN₁ may be updated depending on a preset value. FIG. 14illustrates an example in which the unreliability value of the variablenode VN₁ increases by 5, which is the flipping function value.Meanwhile, when the updated unreliability value is less than the thirdthreshold value, the hard decision value of the variable node VN₁ may bemaintained. Because the updated unreliability value (5) is less than thethird threshold value (20), the hard decision value of the variable nodeVN₁ may be maintained.

For the variable node VN₂, the unreliability value may be updated basedon the same principle.

FIG. 15 is an example diagram illustrating the process of modifying asymbol according to an embodiment of the disclosed technology.

In the embodiment described with reference to FIG. 15, it is assumedthat the unreliability value of the variable node VN₁ and that of thevariable node VN₂ are updated to 15 and 9, respectively, becauseadditional iterations are performed after the unreliability values areupdated as described with reference to FIG. 14.

It is assumed that the reliability values of the candidate symbolscorresponding to the variable nodes VN₁ and VN₂ are updated based on theC2V messages received by the variable nodes VN₁ and VN₂. In theembodiment described with reference to FIG. 15, the reliability valuesof the candidate symbols may be updated based on a similar or sameprinciple as in the above-described embodiments, and a detaileddescription thereof will be omitted.

In the embodiment described with reference to FIG. 15, the targetcandidate symbol is the candidate symbol whose updated reliability valueis the largest among the updated reliability values, and the firstfunction value is assumed to be the updated reliability value of thetarget candidate symbol. Also, the second function value is assumed tobe the second largest value among the updated reliability values.

In the example of FIG. 15, for the variable node VN₁, the flippingfunction value becomes 6, which is calculated by subtracting the secondfunction value (12) from the first function value (18), based on asimilar or same principle as in the above-described embodiments. Whenthe flipping function value is equal to or greater than the secondthreshold value and less than the first threshold value, theunreliability value of the variable node VN₁ may increase by a presetvalue. FIG. 15 illustrates an example in which the unreliability valueof the variable node VN₁ increases by 6, which is the flipping functionvalue. When the updated unreliability value is equal to or greater thanthe third threshold value, the hard decision value of the variable nodeVN₁ may be modified. Because the updated unreliability value (21) isequal to or greater than the third threshold value (20), the harddecision value of the variable node VN₁ may be changed to the targetcandidate symbol, that is, ‘0’. FIG. 15 illustrates an example in whichthe unreliability value of the variable node VN₁ is changed to 0 from 21after the hard decision value of the variable node VN₁ is modified basedon the unreliability value. In an embodiment, even though the harddecision value of the variable node VN₁ is modified based on theunreliability value, the unreliability value of the variable node VN₁may not be changed to the initial value 0, as described above.

Because the flipping function value of the variable node VN₂ is 2 and isless than the second threshold value (4), both the hard decision valueand the unreliability value of the variable node VN₂ may be maintained.

FIG. 16 is a flowchart illustrating the method of operating an errorcorrection decoder according to an embodiment of the disclosedtechnology.

According to an embodiment, at least one of the steps illustrated inFIG. 16 may be skipped, and the order in which the steps are performedmay be changed. For example, step 1607 may be skipped, and step 1607 maybe performed before step 1605.

At step 1601, the error correction decoder may receive a read vectorcorresponding to a codeword.

At step 1603, the error correction decoder may form symbols by groupingthe read values included in the read vector, and may assign the formedsymbols to variable nodes, respectively.

At steps 1605 to 1611, the i-th iteration may be performed.

At step 1605, the error correction decoder may set the reliabilityvalues of candidate symbols corresponding to each of the variable nodes.For each of the variable nodes, the error correction decoder may set thereliability values of the candidate symbols, which can be selected asthe hard decision value of the variable node based on the initial symbolassigned to the variable node. In an embodiment, the error correctiondecoder may set the reliability values of the candidate symbols inconsideration of at least one of the hamming distance from the initialsymbol, the number of the iteration, or the number of UCNs correspondingto the previous iteration. When the first iteration is performed, theunreliability value of each of the variable nodes may be set to aninitial value.

At step 1607, the error correction decoder may set or change thresholdvalues, which are criteria for determining whether to modify the harddecision value of the variable node. In the first iteration, the errorcorrection decoder may set the threshold values in consideration of thedegree of the variable node. In the iterations after the firstiteration, the error correction decoder may change the threshold valuesin consideration of at least one of the number of the iteration or thenumber of UCNs corresponding to the previous iteration.

At step 1609, the error correction decoder may update the reliabilityvalues of the candidate symbols. For example, the error correctiondecoder may update the reliability values of the candidate symbols foreach of the variable nodes depending on the C2V messages received by thevariable nodes.

At step 1611, the error correction decoder may modify or maintain thehard decision values of the variable nodes. Step 1611 will be describedin detail with reference to FIGS. 17 to 19.

At step 1613, the error correction decoder may perform a syndrome checkusing the hard decision vector, that is, the hard decision values, whichare modified or maintained at step 1611. If the syndrome check haspassed (in case of ‘Y’), step 1615 may be performed, and if not (in caseof ‘N’), step 1621 may be performed.

At step 1615, the error correction decoder may output the hard decisionvector as a decoded codeword.

Meanwhile, at step 1621, the error correction decoder may check whetherthe number of iterations that are performed reaches the maximum numberof iterations (I). If the number of iterations that are performedreaches the maximum number of iterations (I) (in case of ‘Y’), step 1623is performed, whereby a fail signal, which indicates that errorcorrection decoding has failed, may be output. If the number ofiterations that are performed does not reach the maximum number ofiterations (I) (in case of ‘N’), the (i+1)-th iteration may be performedafter passing through step 1631.

FIG. 17 is a flowchart illustrating the method of operating an errorcorrection decoder according to an embodiment of the disclosedtechnology.

Steps 1701 to 1709 illustrated in FIG. 17 may be performed for each ofthe variable nodes.

Steps 1701 to 1709 illustrated in FIG. 17 may be performed when anunreliability value is not used.

At step 1701, the error correction decoder may calculate a firstfunction value, which is related to the updated reliability value of thetarget candidate symbol.

At step 1703, the error correction decoder may calculate a secondfunction value, which is related to one or more of updated reliabilityvalues of the remaining candidate symbols other than the targetcandidate symbol.

At step 1705, the error correction decoder may calculate a flippingfunction value. The flipping function value may be a value that iscalculated by subtracting the second function value from the firstfunction value.

At step 1707, the error correction decoder may determine whether theflipping function value is equal to or greater than a first thresholdvalue. If the flipping function value is equal to or greater than thefirst threshold value (in case of ‘Y’), step 1709 may be performed, andif not, step 1613 may be performed.

At step 1709, the error correction decoder may change the hard decisionvalue of the variable node to the target candidate symbol.

According to an embodiment, the error correction decoder may check atstep 1705 whether the flipping function value is a positive number, andmay skip step 1707 and step 1709 when the flipping function value is nota positive number.

FIG. 18 is a flowchart illustrating the method of operating an errorcorrection decoder according to an embodiment of the disclosedtechnology.

Steps 1801 to 1821 illustrated in FIG. 18 may be performed for each ofthe variable nodes.

Steps 1801 to 1821 illustrated in FIG. 18 may be performed when one bitis allocated in order to represent an unreliability value.

At step 1801, the error correction decoder may calculate a firstfunction value, which is related to the updated reliability value of thetarget candidate symbol.

At step 1803, the error correction decoder may calculate a secondfunction value, which is related to one or more of updated reliabilityvalues of the remaining candidate symbols other than the targetcandidate symbol.

At step 1805, the error correction decoder may calculate a flippingfunction value. The flipping function value may be a value that iscalculated by subtracting the second function value from the firstfunction value.

At step 1807, the error correction decoder may determine whether theflipping function value is equal to or greater than a first thresholdvalue. If the flipping function value is equal to or greater than thefirst threshold value (in case of ‘Y’), step 1809 may be performed, andif not, step 1811 may be performed.

At step 1809, the error correction decoder may change the hard decisionvalue of the variable node to the target candidate symbol. Here, theerror correction decoder checks whether the unreliability value ischanged from an initial value in the variable node, and may set theunreliability value to the initial value when the unreliability value ofthe variable node is changed.

At step 1811, the error correction decoder may determine whether theflipping function value is equal to or greater than a second thresholdvalue, which is less than the first threshold value. If the flippingfunction value is equal to or greater than the second threshold value(in case of ‘Y’), step 1813 may be performed, and if not, step 1613 maybe performed.

At step 1813, the error correction decoder may determine whether theunreliability value of the variable node is changed from the initialvalue. If the unreliability value of the variable node is changed (incase of ‘Y’), step 1815 may be performed, and if not (in case of ‘N’),step 1821 may be performed.

At step 1815, the error correction decoder may change the hard decisionvalue of the variable node to the target candidate symbol. In anembodiment, the error correction decoder may set the unreliability valueof the variable node to the initial value after it changes the harddecision value of the variable node to the target candidate symbol. Inan embodiment, the error correction decoder may not set theunreliability value of the variable node to the initial value after itchanges the hard decision value of the variable node to the targetcandidate symbol. This is because, when the flipping function value isequal to or greater than the second threshold value and less than thefirst threshold value, the target candidate symbol is a less accurateestimate for the variable node, compared to when the flipping functionvalue is equal to or greater than the first threshold value.Accordingly, in this case, the unreliability value of the variable nodemay not be set to the initial value in order to easily modify the harddecision value of the variable node later.

At step 1821, the error correction decoder may change the unreliabilityvalue of the variable node.

Meanwhile, according to an embodiment, the error correction decoder maycheck at step 1805 whether the flipping function value is a positivenumber, and may determine whether the absolute value of the flippingfunction value is equal to or greater than the first threshold value bycomparing the absolute value of the flipping function value with thefirst threshold value when the flipping function value is not a positivenumber. For example, when the first function value is related to thelargest value, among the remaining updated reliability values other thanthe updated reliability value of the candidate symbol that is the sameas the hard decision value of the variable node, and when the secondfunction value is related to the updated reliability value of thecandidate symbol that is the same as the hard decision value of thevariable node, if the flipping function value is not a positive numberand the absolute value thereof is equal to or greater than the firstthreshold value, this may mean that the hard decision value of thevariable node is a more accurate estimate. Therefore, in this case,steps 1807 to 1821 may be skipped, and the error correction decoder maynot modify the hard decision value of the variable node. Here, the errorcorrection decoder may check whether the unreliability value of thevariable node is changed from the initial value, and may set theunreliability value of the variable node to the initial value if theunreliability value of the variable node is changed from the initialvalue.

FIG. 19 is a flowchart illustrating the method of operating an errorcorrection decoder according to an embodiment of the disclosedtechnology.

Steps 1901 to 1917 illustrated in FIG. 19 may be performed for each ofthe variable nodes.

Steps 1901 to 1917 illustrated in FIG. 19 may be performed when two ormore bits are allocated in order to represent an unreliability value.

At step 1901, the error correction decoder may calculate a firstfunction value, which is related to the updated reliability value of thetarget candidate symbol.

At step 1903, the error correction decoder may calculate a secondfunction value, which is related to one or more of updated reliabilityvalues of the remaining candidate symbols other than the targetcandidate symbol.

At step 1905, the error correction decoder may calculate a flippingfunction value. The flipping function value may be a value that iscalculated by subtracting the second function value from the firstfunction value.

At step 1907, the error correction decoder may determine whether theflipping function value is equal to or greater than the first thresholdvalue. If the flipping function value is equal to or greater than thefirst threshold value (in case of ‘Y’), step 1909 may be performed, andif not, step 1911 may be performed.

At step 1909, the error correction decoder may change the hard decisionvalue of the variable node to the target candidate symbol. In anembodiment, the error correction decoder may check whether theunreliability value is changed from an initial value in the variablenode, and may set the unreliability value of the variable node to theinitial value when the unreliability value is changed from the initialvalue in the variable node.

At step 1911, the error correction decoder may determine whether theflipping function value is equal to or greater than a second thresholdvalue, which is less than the first threshold value. If the flippingfunction value is equal to or greater than the second threshold value(in case of ‘Y’), step 1913 may be performed, and if not, step 1613 maybe performed.

At step 1913, the error correction decoder may update the unreliabilityvalue of the variable node. For example, the error correction decodermay increase the unreliability value of the variable node by a presetvalue.

At step 1915, the error correction decoder may determine whether theupdated unreliability value is equal to or greater than a thirdthreshold value. If the updated unreliability value is equal to orgreater than the third threshold value (in case of ‘Y’), step 1917 maybe performed, and if not (in case of ‘N’), step 1613 may be performed.

At step 1917, the error correction decoder may change the hard decisionvalue of the variable node to the target candidate symbol. In anembodiment, the error correction decoder may set the unreliability valueof the variable node to the initial value after it changes the harddecision value of the variable node to the target candidate symbol. Inan embodiment, the error correction decoder may not set theunreliability value of the variable node to the initial value after itchanges the hard decision value of the variable node to the targetcandidate symbol, as described at step 1815 of FIG. 18.

Meanwhile, according to an embodiment, the error correction decoder maycheck at step 1905 whether the flipping function value is a positivenumber, and may determine whether the absolute value of the flippingfunction value is equal to or greater than the first threshold value bycomparing the absolute value of the flipping function value with thefirst threshold value when the flipping function value is not a positivenumber. When the absolute value of the flipping function value is equalto or greater than the first threshold value, steps 1907 to 1917 may beskipped based on the same principle as that described with reference toFIG. 18, and the error correction decoder may not modify the harddecision value of the variable node. Here, the error correction decodermay check whether the unreliability value of the variable node ischanged from the initial value, and may set the unreliability value ofthe variable node to the initial value if the unreliability value of thevariable node is changed from the initial value.

FIG. 20 is a diagram illustrating a memory system according to anembodiment of the disclosed technology.

Referring to FIG. 20, a memory system 2000 may include a memory device2200 which stores data, and a memory controller 2100 which controls thememory device 2200 in response to a request received from a host 1000.

The host 1000 may be a device or a system which stores data in thememory system 2000 or retrieves data from the memory system 2000. Forexample, the host 1000 may include at least one of a computer, aportable digital device, a tablet, a digital camera, a digital audioplayer, a television, a wireless communication device, or a cellularphone, but embodiments of the disclosed technology are not limitedthereto.

The memory controller 2100 may control the overall operation of thememory system 2000. The memory controller 2100 may perform variousoperations in response to requests received from the host 1000. Forexample, the memory controller 2100 may perform a program operation, aread operation, an erase operation, etc. on the memory device 2200.During a program operation, the memory controller 2100 may transmit aprogram command, an address, a codeword, etc. to the memory device 2200.During a read operation, the memory controller 2100 may transmit a readcommand, an address, etc. to the memory device 2200, and may receiveread data corresponding to a codeword from the memory device 2200.During an erase operation, the memory controller 2100 may transmit anerase command, an address, etc. to the memory device 2200.

The memory controller 2100 may include a host interface 2110, a centralprocessing unit (CPU) 2120, a memory interface 2130, a buffer memory2140, an error correction circuit 2150, and an internal memory 2160. Thehost interface 2110, the memory interface 2130, the buffer memory 2140,the error correction circuit 2150, and the internal memory 2160 may becontrolled by the CPU 2120.

The host interface 2110 may transfer a program request, a read request,and an erase request, which are received from the host 1000, to the CPU2120. During a program operation, the host interface 2110 may receiveoriginal data, corresponding to the program request, from the host 1000,and may store the received original data in the buffer memory 2140.During a read operation, the host interface 2110 may transmit a decodedcodeword, stored in the buffer memory 2140, to the host 1000. The hostinterface 2110 may communicate with the host 1000 using variousinterface protocols. For example, the host interface 2110 maycommunicate with the host 1000 using at least one of interfaceprotocols, such as Non-Volatile Memory express (NVMe), PeripheralComponent Interconnect-Express (PCI-E), Advanced Technology Attachment(ATA), Serial ATA (SATA), Parallel ATA (PATA), Universal Serial Bus(USB), Multi-Media Card (MMC), Enhanced Small Disk Interface (ESDI),Integrated Drive Electronics (IDE), Mobile Industry Processor Interface(MIPI), Universal Flash Storage (UFS), Small Computer System Interface(SCSI), or serial attached SCSI (SAS), but embodiments of the disclosedtechnology are not limited thereto.

The CPU 2120 may perform various types of calculations (operations) orgenerate commands and addresses so as to control the memory device 2200.For example, the CPU 2120 may generate various commands and addressesrequired for a program operation, a read operation, and an eraseoperation in response to requests received through the host interface2110.

When the program request is received through the host interface 2110,the CPU 2120 may control the error correction circuit 2150 so that errorcorrection encoding is performed on the original data stored in thebuffer memory 2140. When notification that a codeword has been generatedis received from the error correction circuit 2150, the CPU 2120 maygenerate the program command and the address, and may control the memoryinterface 2130 so that the generated program command and address and thecodeword stored in the buffer memory 2140 are transmitted to the memorydevice 2200.

When the read request is received through the host interface 2110, theCPU may generate the read command and the address, and may control thememory interface 2130 so that the generated read command and address aretransmitted to the memory device 2200. When notification that the readdata has been received is received from the memory interface 2130, theCPU 2120 may control the error correction circuit 2150 so that errorcorrection decoding is performed on the read data stored in the buffermemory 2140. When notification that a decoded codeword has beengenerated is received from the error correction circuit 2150, the CPU2120 may control the host interface 2110 so that the decoded codewordstored in the buffer memory 2140 is transmitted to the host 1000.

The memory interface 2130 may communicate with the memory device 2200using various interface protocols.

During a program operation, the memory interface 2130 may transmit theprogram command and the address, received from the CPU 2120, and thecodeword, stored in the buffer memory 2140, to the memory device 2200.

During a read operation, the memory interface 2130 may transmit the readcommand and the address, received from the CPU 2120, to the memorydevice 2200. During the read operation, the memory interface 2130 maystore read data, received from the memory device 2200, in the buffermemory 2140, and may notify the CPU 2120 that the read data has beenreceived.

The buffer memory 2140 may temporarily store data while the memorycontroller 2100 controls the memory device 2200.

During a program operation, the buffer memory 2140 may store theoriginal data received from the host 1000 through the host interface2110 and transmit the stored original data to the error correctioncircuit 2150. During the program operation, the buffer memory 2140 maystore the codeword received from the error correction circuit 2150, andmay provide the stored codeword to the memory interface 2130.

During a read operation, the buffer memory 2140 may store the read datareceived from the memory device 2200 through the memory interface 2130and provide the stored read data to the error correction circuit 2150.During the read operation, the buffer memory 2140 may store the decodedcodeword received from the error correction circuit 2150, and mayprovide the stored decoded codeword to the host interface 2110.

The error correction circuit 2150 may perform error correction encodingon the original data, and may perform error correction decoding on theread data. The error correction circuit 2150 may have error correctioncapability at a predetermined level. For example, when a number of errorbits, which do not exceed the error correction capability, are presentin the read data, the error correction circuit 2150 may detect andcorrect the error included in the read data. The error correctionencoder 2150 may be an error correction circuit which uses low-densityparity check (LDPC) codes, especially, non-binary LDPC (NB-LDPC) codes,as the error correction code, but embodiments of the disclosedtechnology are not limited thereto.

The error correction circuit 2150 may include an error correctionencoder 2152 and an error correction decoder 2154.

The error correction encoder 2152 may generate a codeword by performingerror correction encoding on the original data stored in the buffermemory 2140. The error correction encoder 2152 may store the generatedcodeword in the buffer memory 2140, and may notify the CPU 2120 that thecodeword has been generated. The basic configuration and operation ofthe error correction encoder 2152 may be identical to those of the errorcorrection encoder 100, described above with reference to FIG. 1.

The error correction decoder 2154 may generate a decoded codeword byperforming error correction decoding on the read data stored in thebuffer memory 2140. The error correction decoder 2154 may store thedecoded codeword in the buffer memory 2140, and may notify the CPU 2120that the decoded codeword has been generated. When error included in theread data cannot be corrected, the error correction decoder 2154 maynotify the CPU 2120 that error correction decoding has failed. The basicconfiguration and operation of the error correction decoder 2154 may beidentical to those of the error correction decoder 200, described abovewith reference to FIG. 1. That is, a symbol generator 2154 a, a nodeprocessor 2154 b, and a syndrome checker 2154 c, which are illustratedin FIG. 20, may perform the same operations as the symbol generator 210,the node processor 220, and the syndrome checker 230, respectively,which are illustrated in FIG. 1.

The internal memory 2160 may be used as a storage which stores varioustypes of information required for the operation of the memory controller2100. The internal memory 2160 may store a plurality of tables. In anembodiment, the internal memory 2160 may store an address mapping tablein which logical addresses are mapped to physical addresses.

The memory device 2200 may perform a program operation, a readoperation, an erase operation, etc. under the control of the memorycontroller 2100. The memory device 2200 may be implemented as a volatilememory device in which stored data is lost when the supply of power isinterrupted or as a nonvolatile memory device in which stored data isretained even when the supply of power is interrupted.

The memory device 2200 may receive the program command, the address, andthe codeword from the memory controller 2100, and may store the codewordin response to the received program command and address.

The memory device 2200 may perform a read operation on the codeword inresponse to the read command and the address received from the memorycontroller 2100, and may provide read data to the memory controller2100.

FIG. 21 is a diagram illustrating a memory device according to anembodiment of the disclosed technology. The memory device illustrated inFIG. 21 may be applied to the memory system illustrated in FIG. 20.

The memory device 2200 may include a control logic 2210, peripheralcircuits 2220 and a memory cell array 2240. The peripheral circuits 2220may include a voltage generator 2222, a row decoder 2224, aninput/output circuit 2226, a column decoder 2228, a page buffer group2232, and a current sensing circuit 2234.

The control logic 2210 may control the peripheral circuits 2220 underthe control of the memory controller 2100 of FIG. 20.

The control logic 2210 may control the peripheral circuits 2220 inresponse to a command CMD and an address ADD that are received from thememory controller 2100 through the input/output circuit 2226. Forexample, the control logic 2210 may output an operation signal OP_CMD, arow address RADD, a column address CADD, page buffer control signalsPBSIGNALS, and an enable bit VRY_BIT<#> in response to the command CMDand the address ADD. The control logic 2210 may determine whether averify operation has passed or failed in response to a pass or failsignal PASS or FAIL received from the current sensing circuit 2234.

The peripheral circuits 2220 may perform a program operation of storingdata in the memory cell array 2240, a read operation of outputting datastored in the memory cell array 2240, and an erase operation of erasingdata stored in the memory cell array 2240.

The voltage generator 2222 may generate various operating voltages Vopthat are used for the program, read, and erase operations in response tothe operation signal OP_CMD received from the control logic 2210. Forexample, the voltage generator 2222 may transfer a program voltage, averify voltage, a pass voltage, a read voltage, an erase voltage, aturn-on voltage, etc. to the row decoder 2224.

The row decoder 2224 may transfer the operating voltages Vop to locallines LL that are coupled to a memory block selected from among memoryblocks included in the memory cell array 2240 in response to the rowaddress RADD received from the control logic 2210. The local lines LLmay include local word lines, local drain select lines, and local sourceselect lines. In addition, the local lines LL may include various lines,such as source lines, coupled to memory blocks.

The input/output circuit 2226 may transfer the command CMD and theaddress ADD, received from the memory controller through input/output(TO) lines, to the control logic 2210, or may exchange data with thecolumn decoder 2228.

The column decoder 2228 may transfer data between the input/outputcircuit 2226 and the page buffer group 2232 in response to a columnaddress CADD received from the control logic 2210. For example, thecolumn decoder 2228 may exchange data with page buffers PB1 to PBmthrough data lines DL or may exchange data with the input/output circuit2226 through column lines CL.

The page buffer group 2232 may be coupled to bit lines BL1 to BLmcoupled in common to the memory blocks BLK1 to BLKi. The page buffergroup 2232 may include a plurality of page buffers PB1 to PBm coupled tothe bit lines BL1 to BLm, respectively. For example, one page buffer maybe coupled to each bit line. The page buffers PB1 to PBm may be operatedin response to the page buffer control signals PBSIGNALS received fromthe control logic 2210. For example, during a program operation, thepage buffers PB1 to PBm may temporarily store program data received fromthe memory controller, and may control voltages to be applied to the bitlines BL1 to BLm based on the program data. Also, during a readoperation, the page buffers PB1 to PBm may temporarily store datareceived through the bit lines BL1 to BLm or may sense voltages orcurrents of the bit lines BL1 to BLm.

During a read operation or a verify operation, the current sensingcircuit 2234 may generate a reference current in response to the enablebit VRY_BIT<#> received from the control logic 2210, and may compare areference voltage, generated by the reference current, with a sensingvoltage VPB, received from the page buffer group 2232, and then output apass signal PASS or a fail signal FAIL.

The memory cell array 2240 may include a plurality of memory blocks BLK1to BLKi in which data is stored. In the memory blocks BLK1 to BLKi, userdata and various types of information required for the operation of thememory device 2200 may be stored. The memory blocks BLK1 to BLKi mayeach be implemented as a two-dimensional (2D) structure or athree-dimensional (3D) structure, and may be equally configured.

FIG. 22 is an example diagram illustrating a memory block.

A memory cell array may include a plurality of memory blocks, and anyone memory block BLKi of the plurality of memory blocks is illustratedin FIG. 22 for convenience of description.

A plurality of word lines arranged in parallel to each other between afirst select line and a second select line may be coupled to the memoryblock BLKi. Here, the first select line may be a source select line SSL,and the second select line may be a drain select line DSL. In detail,the memory block BLKi may include a plurality of strings ST coupledbetween bit lines BL1 to BLm and a source line SL. The bit lines BL1 toBLm may be coupled to the strings ST, respectively, and the source lineSL may be coupled in common to the strings ST. The strings ST may beequally configured, and thus the string ST coupled to the first bit lineBL1 will be described in detail by way of example.

The string ST may include a source select transistor SST, a plurality ofmemory cells F1 to F16, and a drain select transistor DST which arecoupled in series to each other between the source line SL and the firstbit line BL1. A single string ST may include at least one source selecttransistor SST and at least one drain select transistor DST, and morememory cells than the memory cells F1 to F16 illustrated in the drawingmay be included in the string ST.

A source of the source select transistor SST may be coupled to thesource line SL, and a drain of the drain select transistor DST may becoupled to the first bit line BL1. The memory cells F1 to F16 may becoupled in series between the source select transistor SST and the drainselect transistor DST. Gates of the source select transistors SSTincluded in different strings ST may be coupled to the source selectline SSL, gates of the drain select transistors DST included indifferent strings ST may be coupled to the drain select line DSL, andgates of the memory cells F1 to F16 may be coupled to a plurality ofword lines WL1 to WL16, respectively. A group of memory cells coupled tothe same word line, among the memory cells included in different stringsST, may be referred to as a “physical page: PPG”. Therefore, the memoryblock BLKi may include a number of physical pages PPG identical to thenumber of word lines WL1 to WL16.

One memory cell may store one bit of data. This cell is called asingle-level cell (SLC). Here, one physical page PPG may store datacorresponding to one logical page LPG The data corresponding to onelogical page LPG may include a number of data bits identical to thenumber of cells included in one physical page PPG For example, when twoor more bits of data are stored in one memory cell, one physical pagePPG may store data corresponding to two or more logical pages LPG Forexample, in a memory device driven in an MLC type, data corresponding totwo logical pages may be stored in one physical page PPG In a memorydevice driven in a TLC type, data corresponding to three logical pagesmay be stored in one physical page PPG

FIG. 23 is a diagram illustrating an embodiment of a memory systemincluding the memory controller of FIG. 20.

Referring to FIG. 23, a memory system 30000 may be implemented as acellular phone, a smartphone, a tablet, a personal computer (PC), apersonal digital assistant (PDA) or a wireless communication device. Thememory system 30000 may include a memory device 2200 and a memorycontroller 2100 that is capable of controlling the operation of thememory device 2200.

The memory controller 2100 may control the data access operation of thememory device 2200, e.g., a program operation, an erase operation or aread operation, under the control of a processor 3100.

Data programmed in the memory device 2200 may be output through adisplay 3200 under the control of the memory controller 2100.

A radio transceiver 3300 may send and receive radio signals through anantenna ANT. For example, the radio transceiver 3300 may change a radiosignal received through the antenna ANT into a signal which may beprocessed by the processor 3100. Therefore, the processor 3100 mayprocess a signal output from the radio transceiver 3300 and transmit theprocessed signal to the memory controller 2100 or the display 3200. Thememory controller 2100 may transmit a signal processed by the processor3100 to the memory device 2200. Furthermore, the radio transceiver 3300may change a signal output from the processor 3100 into a radio signal,and output the changed radio signal to the external device through theantenna ANT. An input device 3400 may be used to input a control signalfor controlling the operation of the processor 3100 or data to beprocessed by the processor 3100. The input device 3400 may beimplemented as a pointing device such as a touch pad or a computermouse, a keypad or a keyboard. The processor 3100 may control theoperation of the display 3200 such that data output from the memorycontroller 2100, data output from the radio transceiver 3300, or dataoutput from the input device 3400 is output through the display 3200.

In an embodiment, the memory controller 2100 capable of controlling theoperation of the memory device 2200 may be implemented as a part of theprocessor 3100 or as a chip provided separately from the processor 3100.

FIG. 24 is a diagram illustrating an embodiment of a memory systemincluding the memory controller of FIG. 20.

Referring to FIG. 24, a memory system 70000 may be embodied in a memorycard or a smart card. The memory system 70000 may include a memorydevice 2200, a memory controller 2100, and a card interface 7100.

The memory controller 2100 may control data exchange between the memorydevice 2200 and the card interface 7100. In an embodiment, the cardinterface 7100 may be a secure digital (SD) card interface or amulti-media card (MMC) interface, but it is not limited thereto.

The card interface 7100 may interface data exchange between a host 60000and the memory controller 2100 according to a protocol of the host60000. In an embodiment, the card interface 7100 may support a universalserial bus (USB) protocol, and an interchip (IC)-USB protocol. Here, thecard interface 7100 may refer to hardware capable of supporting aprotocol which is used by the host 60000, software installed in thehardware, or a signal transmission method.

When the memory system 70000 is connected to a host interface 6200 ofthe host 60000 such as a PC, a tablet, a digital camera, a digital audioplayer, a cellular phone, console video game hardware or a digitalset-top box, the host interface 6200 may perform data communication withthe memory device 2200 through the card interface 7100 and the memorycontroller 2100 under the control of a microprocessor 6100.

In accordance with the disclosed technology, the error correctioncapabilities of an error correction decoder using NB-LDPC codes and amemory system having the error correction decoder can be improved.

What is claimed is:
 1. An error correction decoder, comprising: a symbolgenerator configured to form an initial symbol and assign the initialsymbol to a variable node; a reliability value manager configured to setreliability values of candidate symbols corresponding to the variablenode based on the initial symbol at the time of a start of a currentiteration and update the reliability values of the candidate symbolsbased on check-to-variable (C2V) messages received by the variable nodein the current iteration; a flipping function value calculatorconfigured to calculate a flipping function value by subtracting asecond function value from a first function value in the currentiteration, the first function value being related to an updatedreliability value of a target candidate symbol, and the second functionvalue being related to one or more of updated reliability values ofremaining candidate symbols other than the target candidate symbol; anda symbol corrector configured to compare the flipping function valuewith a first threshold value in the current iteration and change a harddecision value of the variable node to the target candidate symbol upona determination that the flipping function value is equal to or greaterthan the first threshold value.
 2. The error correction decoderaccording to claim 1, wherein the flipping function value calculator isconfigured to select the target candidate symbol among the candidatesymbols, the target candidate symbol having the largest updatedreliability value among updated reliability values of the candidatesymbols.
 3. The error correction decoder according to claim 2, wherein:the first function value is the largest value among the updatedreliability values of the candidate symbols or the largest value towhich at least one of a scaling factor or a scaling offset is applied,and the second function value is a second largest value among theupdated reliability values of the candidate symbols or a value obtainedby adding the updated reliability values of at least two of theremaining candidate symbols.
 4. The error correction decoder accordingto claim 2, wherein: the first function value is the largest value amongthe updated reliability values of the candidate symbols or the largestvalue to which at least one of a scaling factor or a scaling offset isapplied, and the second function value is an updated reliability valueof a candidate symbol that is identical to the hard decision value ofthe variable node.
 5. The error correction decoder according to claim 1,wherein the flipping function value calculator is configured to selectthe target candidate symbol among the candidate symbols, the targetcandidate symbol having the largest updated reliability value amongremaining updated reliability values other than an updated reliabilityvalue of a candidate symbol that is identical to the hard decision valueof the variable node.
 6. The error correction decoder according to claim5, wherein the flipping function value calculator is configured toselect the target candidate symbol based on a comparison among updatedreliability values of remaining candidate symbols other than thecandidate symbol that is identical to the hard decision value of thevariable node.
 7. The error correction decoder according to claim 5,wherein: the first function value is the largest value among theremaining updated reliability values or the largest value to which atleast one of a scaling factor or a scaling offset is applied, and thesecond function value is the updated reliability value of the candidatesymbol that is identical to the hard decision value of the variablenode.
 8. The error correction decoder according to claim 7, wherein thesymbol corrector is configured to skip the comparison of the flippingfunction value with the first threshold value upon a determination thatthe flipping function value is not a positive number.
 9. The errorcorrection decoder according to claim 1, wherein the reliability valuemanager is configured to set the reliability values of the candidatesymbols to be different from one another, the candidate symbols havingdifferent hamming distances from one another.
 10. The error correctiondecoder according to claim 1, wherein the reliability value manager isconfigured to set the reliability values of the candidate symbols basedon an iteration number of the current iteration.
 11. The errorcorrection decoder according to claim 1, wherein the reliability valuemanager is configured to set the reliability values of the candidatesymbols based on a number of unsatisfied check nodes (UCNs)corresponding to a previous iteration performed before the currentiteration.
 12. The error correction decoder according to claim 1,further comprising: a threshold value manager configured to set thefirst threshold value based on at least one of a degree of the variablenode, a number of the iteration, or a number of unsatisfied check nodes(UCNs) corresponding to a previous iteration performed before thecurrent iteration.
 13. The error correction decoder according to claim1, wherein the symbol corrector is configured to: check whether anunreliability value of the variable node is changed from an initialvalue upon a determination that the flipping function value is equal toor greater than a second threshold value and less than the firstthreshold value, the second threshold value being less than the firstthreshold value, update the unreliability value upon a determinationthat the unreliability value is not changed from the initial value as aresult of checking, and change the hard decision value of the variablenode to the target candidate symbol upon a determination that theunreliability value is changed from the initial value as the result ofchecking.
 14. The error correction decoder according to claim 13,wherein the symbol corrector is configured to change the hard decisionvalue of the variable node to the target candidate symbol and maintainthe changed unreliability value upon a determination that the flippingfunction value is equal to or greater than the second threshold valueand less than the first threshold value and the unreliability value ischanged from the initial value.
 15. The error correction decoderaccording to claim 13, wherein the symbol corrector is configured tochange the hard decision value of the variable node to the targetcandidate symbol and set the unreliability value to the initial valueupon a determination that the flipping function value is equal to orgreater than the first threshold value in a subsequent iterationproceeding after the current iteration.
 16. The error correction decoderaccording to claim 13, wherein the symbol corrector is configured tomaintain the hard decision value of the variable node and set theunreliability value to the initial value upon a determination that theflipping function value is a negative number and an absolute value ofthe flipping function value is equal to or greater than the firstthreshold value in a subsequent iteration proceeding after the currentiteration.
 17. The error correction decoder according to claim 1,wherein the symbol corrector is configured to: increase an unreliabilityvalue of the variable node by a preset value upon a determination thatthe flipping function value is equal to or greater than a secondthreshold value and less than the first threshold value, the secondthreshold value being less than the first threshold value, and changethe hard decision value of the variable node to the target candidatesymbol upon a determination that the increased unreliability value isequal to or greater than a third threshold value.
 18. The errorcorrection decoder according to claim 1, wherein the symbol corrector isconfigured to: increase an unreliability value of the variable node by afirst preset value upon a determination that the flipping function valueis equal to or greater than a second threshold value and less than thefirst threshold value, the second threshold value being less than thefirst threshold value, increase the unreliability value by a secondpreset value, which is less than the first preset value, upon adetermination that the flipping function value is equal to or greaterthan a third threshold value and less than the second threshold value,the third threshold value being less than the second threshold value,and change the hard decision value of the variable node to the targetcandidate symbol upon a determination that the increased unreliabilityvalue is equal to or greater than a fourth threshold value.
 19. A memorysystem, comprising: a memory device; and a memory controller incommunication with the memory device and including an error correctiondecoder configured to perform an error correction decoding usingnon-binary low-density parity check (NB-LDPC) codes based on a readvector received from the memory device, wherein the error correctiondecoder comprises: a symbol generator configured to form an initialsymbol by grouping read values included in the read vector and assignthe initial symbol to a variable node; a reliability value managerconfigured to set reliability values of candidate symbols correspondingto the variable node based on the initial symbol at the time of a startof a current iteration and update the reliability values of thecandidate symbols based on check-to-variable (C2V) messages received bythe variable node in the current iteration; a flipping function valuecalculator configured to calculate a flipping function value bysubtracting a second function value from a first function value in thecurrent iteration, the first function value being related to an updatedreliability value of a target candidate symbol, and the second functionvalue being related to one or more of updated reliability values ofremaining candidate symbols other than the target candidate symbol; anda symbol corrector configured to compare the flipping function valuewith a first threshold value in the current iteration and change a harddecision value of the variable node to the target candidate symbol upona determination that the flipping function value is equal to or greaterthan the first threshold value.
 20. The memory system according to claim19, wherein the flipping function value calculator is configured toselect the target candidate symbol among candidate symbols, the targetcandidate symbol having the largest updated reliability value among theupdated reliability values of the candidate symbols or among remainingupdated reliability values other than an updated reliability value of acandidate symbol that is identical to the hard decision value of thevariable node.